58 results about "Cold wave" patented technology

The invention discloses a power load prediction method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring feature data related to a power load variable in a historical time period; based on weather forecast data in the feature data, a target scene type corresponding to the current load prediction scene is identified, and the target scene type is at least a daily load prediction scene type or a cold-wave weather load prediction scene type; a target power load prediction model corresponding to the target scene type is called to predict the feature data to obtain a prediction result, and the target power load prediction model at least comprises a daily power load prediction model corresponding to the daily load prediction scene type and a daily power load prediction model corresponding to the daily load prediction scene type. Or a cold-wave weather power load prediction model corresponding to the cold-wave weather load prediction scene type, wherein a prediction result is used for at least representing a power load value in a future period of time. The technical problem of low accuracy of power load prediction is solved.

The invention discloses a wind field-unit two-stage power characteristic prediction method for a cold wave meteorological condition. The method comprises the following steps: establishing a cold-wave weather identification model to realize accurate identification of cold-wave weather; a wind speed-power characteristic curve with corrected environmental parameters is constructed, and a CFD simulation model of each fan set is established; then, a unit power prediction model is established in combination with flow field characteristics and meteorological data, and the accuracy and robustness of the model are improved by adopting an optimization algorithm; and constructing an overall power prediction model of the wind field through a machine learning algorithm LightGBM, and obtaining predicted power of the overall wind field and the unit. According to the method, the precision and reliability of wind power prediction can be remarkably improved, and the operation risk of a wind power plant and unstable factors of a power system caused by prediction errors are reduced. The method has the functions of accurately predicting and evaluating the performance of the wind turbine generator under different cold weather conditions, and providing powerful guarantee for optimized operation of a wind power plant and stable operation of a power system.

The invention discloses a multi-scene coupled extreme weather adaptive power system planning method and system, belongs to the technical field of power system planning, and aims to solve the problem that traditional planning does not fully consider extreme weather influence and does not integrate multi-energy complementarity. Comprising the steps of collecting installed capacity, historical weather and power load data of a power grid unit; a conventional weather scene set is generated through clustering by adopting a kmeans algorithm, and an extreme weather scene set is generated in combination with types of extreme high temperature, cold waves and the like; constructing operation models and policy constraints of the thermal power unit, the new energy unit and the electrochemical energy storage unit; based on the planning-operation coupling framework, establishing a two-stage stochastic programming model with the goal of minimizing the total investment and operation cost; and integrating the multi-scene solving model, and outputting an adjustment power supply planning scheme. Through multi-scene coupling and two-stage optimization, planning and operation collaboration is realized, the economy, safety and adaptability of the power system in extreme weather are improved, and technical support is provided for low-carbon transformation.

The invention discloses a smart home energy control system based on energy consumption prediction. According to the invention, through the adaptive learning optimization module, dynamic precision of energy management and high adaptation of user demands are realized. The system can continuously learn user living habits and environment change rules, automatically recognize behavior modes such as morning lighting starting preference and summer air conditioner temperature setting intervals, and generate an equipment control strategy considering both economical efficiency and comfort in combination with real-time electricity price fluctuation and a power grid load state. Under the scene of sudden power grid demand response or extreme weather, the system can quickly adjust the operation priority of equipment, for example, the system can automatically delay starting of the washing machine in the electricity price peak period or preheat the indoor temperature before cold waves come, so that energy waste is avoided, and the living quality is maintained. According to the active control mode based on prediction, the passive response of a traditional smart home is converted into prospective optimization, and the energy use efficiency is remarkably improved.

The invention relates to the technical field of plant ecological carbon sink measurement and calculation, in particular to a tea garden carbon reserve dynamic monitoring and prediction method based on meteorological data, a typical sample plot in a tea garden is selected, meteorological data and basic carbon reserve of the typical sample plot are acquired, and the meteorological data comprise real-time data and forecast data; establishing dynamic monitoring, and performing heat damage identification and freeze damage identification on real-time data; establishing a carbon reserve dynamic correction model; a carbon reserve prediction model is established, and when heat damage and / or freezing damage occur in prediction data, the carbon reserve dynamic correction model is used for prediction to obtain a prediction result; according to the method, the damage of high-temperature stress to the tea trees has a gradual superposition characteristic, high-temperature inhibits photosynthesis, so that calculation is performed in a summation form, cold waves increase respiratory consumption, and the damage mainly depends on the intensity peak value and the cooling rate of a single event; by providing more accurate dynamic measurement and calculation as the supplement of static measurement and calculation, the change condition of the carbon reserves of the tea garden can be reflected in time.

The invention belongs to the technical field of wind power climbing event prediction, and particularly relates to a wind power climbing event prediction method considering extreme weather and time-space correlation information. The method comprises the following steps: acquiring historical actually measured meteorological data of each wind power station of a cluster; carrying out cold-wave weather event identification on historical actually measured meteorological data, generating an antagonistic network based on a time sequence, and carrying out cold-wave event sample expansion; an extreme learning machine is constructed, and cold-wave weather prediction is carried out; performing historical climbing event detection on historical power output results of each station of the cluster; dividing the climbing events into various climbing conditions with different severity degrees by using a K-Means clustering algorithm; and carrying out climbing event prediction. According to the method, sample support is provided for training of the climbing prediction model, the climbing events are clustered and divided by fusing the fan operation state and the climbing characteristics, and the harm degrees of different climbing events, especially the climbing events in extreme weather, are finely measured.

The invention relates to a method for improving the power supply capacity of a cluster wind power and thermodynamic system in cold-wave weather, which belongs to the technical field of power system operation scheduling, and comprises the following steps: constructing an electrothermal coupling system model, considering the operation cost of each device of the system, and forming a comprehensive cost minimization objective function; considering the operation characteristics of the wind power plant under cold waves, introducing external reserve, and establishing a system safe operation constraint system; a Wasserstein distance is adopted to construct a fuzzy set, a probability distribution spherical set driven by sample data is utilized to describe the uncertainty of wind power prediction errors, a model is reconstructed, and a distribution robust optimization model is established; converting an inner-layer extreme value problem into a single-layer optimization problem by applying a duality theory, performing convex approximation processing on opportunity constraints in combination with a conditional value-at-risk theory, and constructing a solvable linear programming model; and designing a dynamic confidence level, and balancing system operation cost and power supply reliability in cold-wave weather by optimizing and coordinating economy and system risks.

The invention discloses a cold-wave weather photovoltaic power generation prediction method under semi-supervised learning, and the method comprises the following steps: (1), obtaining the historical power generation data and the same-period meteorological data of a photovoltaic power station, and defining a cold-wave discrimination standard based on the sudden temperature drop characteristics; (2) detecting and removing abnormal values by adopting Grubbs inspection, complementing missing data through a segmented cubic Hermite interpolation method, and constructing a cold wave small sample data set; (3) performing data expansion on the cold wave small samples by using an SMOTE algorithm, screening high-confidence samples in combination with K-medoids clustering, and training and initializing a CNN-LSTM model by using labeled data; (4) optimizing CNN-LSTM model parameters by using a whale optimization algorithm WOA, and finally outputting a photovoltaic output prediction result and performing error verification; the method is of great significance to technical research in the fields of new energy power prediction, power grid dispatching and the like.

The present disclosure relates to a kind of multi-extreme weather adapted offshore electric-hydrogen-alcohol system multi-energy flow failure modeling and supply-demand evaluation method and device.The method includes: constructing system success flow model based on GO method, identifying key equipment failure transmission chain;GO chart is converted into fault tree, combined with the minimum cut set method and sequential Monte Carlo method Quantification system failure probability and equipment contribution;Build wind and light hydrogen alcohol power model, introduce operation control strategy, dynamically evaluate power and fuel supply and demand reliability index;Establish fault-supply coupling mechanism, under the extreme weather such as typhoon, cold wave, strong wind and rain, the output of wind and light and equipment failure rate are doubly corrected, and a three-dimensional comprehensive reliability index system is constructed.The present application realizes the complete causal chain modeling from equipment failure to supply shortage, improves the accuracy and comprehensiveness of system comprehensive reliability evaluation under multi-extreme weather scenario, and can be used for system optimization design, operation decision and risk prevention and control.

The invention discloses a wind power prediction method, system and device based on cold wave identification and a medium, and relates to the technical field of wind power prediction. The method comprises the following steps: acquiring meteorological data of a target area and operation data of a wind turbine generator, and constructing a cold wave discrimination standard; generating a cold wave sample based on a cold wave discrimination standard and a sample enhancement method; constructing an improved U-net-based cold-wave weather identification model and a cold-wave continuity event-based classification optimization method; performing cold-wave identification on the meteorological data to be detected by using the cold-wave weather identification model and a classification optimization method to obtain a cold-wave identification result in a future prediction period; constructing a wind power combined prediction model; and according to a cold wave identification result of a future prediction period, inputting the data into a corresponding prediction model, and combining output results of the two models to obtain a complete wind power prediction result. According to the invention, the identification of future cold-wave weather can be enhanced, and the accuracy of wind power prediction in the cold-wave weather can be improved.

The application discloses an extreme weather prediction and identification method based on multi-model integration and machine learning coupling, and belongs to the field of climate-adaptive building design and building environment engineering. The technical scheme comprises the following steps: taking the GCM data set of CMIP6 and the RCM data set of CORDEX as original data, combining ERA5 reanalysis data to perform deviation correction and time downscaling processing; constructing a prediction model integrating random forest algorithm and SSP-RCP multi-scenario framework, dividing training set and test set according to time sequence, and realizing multi-scale prediction after standardization and super parameter optimization; constructing an extreme weather identification system based on quantile threshold method, dividing heat wave and cold wave threshold, and quantifying the duration, intensity and severity thereof; and finally generating a high-granularity regional future extreme weather data set for 2024-2034. The application can provide high-precision and high-temporal and spatial resolution meteorological data, provide support for building simulation and climate-adaptive building design, and has good regional adaptability and promotion potential.

The invention discloses a wind-proof, dust-proof, water-absorbing and condensing wind barrier device for an orchard in an arid region, relates to the technical field of orchard protection equipment, and mainly aims to provide the wind-proof, dust-proof, water-absorbing and condensing wind barrier device which can effectively prevent dust, wind and cold waves according to the current environment. According to the main technical scheme, the wind-proof, dust-proof and water-absorbing condensation wind barrier device for the orchard in the arid region comprises a wind barrier component and a telescopic frame body arranged on the ground, a driving component is connected to the telescopic frame body, and a wind barrier net component is installed on the telescopic frame body and comprises a water-absorbing condensation net body and a water-absorbing condensation coating. The water-absorbing condensation coating covers the surface of the water-absorbing condensation net body; a monitoring component; the control component and the central control module are respectively connected to the driving component, the wind speed sensor, the dust concentration sensor, the humidity sensor and the temperature sensor, the transmission module is arranged on the central control module, and the remote control module is in signal connection with the transmission module. The method is mainly used for protecting fruit forests.

The utility model discloses a water meter anti-freezing machine core which comprises an impeller box and a filter screen, and air grooves are formed in flow guide blocks beside a water outlet of the impeller box. The sealing ring is arranged between the impeller box and the filter screen; and the upper end of the sealing ring is provided with a plugging lug protruding into the air groove to seal the air groove. The water meter shell has the advantages that after the water meter shell is assembled, the blocking protruding block protrudes into the air groove to seal the air groove to form a good sealed space, so that a plurality of breathing air bags are formed, it can be effectively guaranteed that air escapes, and meanwhile the anti-freezing capacity of the machine core is improved; the water meter is prevented from bursting due to volume expansion of water in the meter shell; and after the water in the water meter is thawed, the air in the air groove recovers to cope with the coming of the next cold wave.

This invention belongs to the field of power system operation and dispatching technology, and provides a method and system for predicting net load during cold waves based on deep learning. The method includes: collecting electricity load data, renewable energy output data, and meteorological data of a target area, and integrating the collected data; performing feature analysis and extraction on the integrated data to obtain cold wave weather interval data; constructing a direct prediction model and an indirect prediction model, and optimizing the hyperparameters of the two models using the integrated data; using the optimized models to predict the net load for future periods, and fusing the prediction results of the two models to obtain the final net load prediction value. This invention significantly improves the accuracy of predicting the sudden change trend of net load during cold waves, providing a more reliable decision-making basis for the safe dispatching of the power grid under extreme weather conditions.

The application relates to a wind farm tower scanning prevention method and device and a wind farm unit system, and relates to the technical field of wind power systems.The wind farm tower scanning prevention method comprises the following steps: acquiring current operation data and first reference operation data of each unit in a target wind farm; outputting a first signal or a second signal according to the current operation data, the first reference operation data and a first preset data threshold; and if the first signal is output, executing a wind farm overall protection strategy.The wind farm tower scanning prevention method, device and wind farm unit system provided in the application can accurately identify the time node of a cold wave attack, timely take countermeasures and eliminate safety hazards.

This invention provides a method for predicting electricity consumption in extreme weather conditions, which relates to the technical field of electricity consumption prediction. The method comprises the following steps: obtaining user electricity consumption data, filtering out electricity consumption data corresponding to extreme weather conditions, and obtaining four extreme weather data sets, including high temperature, cold wave, rainstorm, and typhoon weather; constructing historical electricity consumption characteristics and meteorological combination characteristics using the four extreme weather data sets; and constructing a TTAO-KAN-LSTM-based electricity consumption prediction model using the historical electricity consumption characteristics and meteorological combination characteristics, and performing training and prediction. This invention employs the aforementioned method for predicting electricity consumption in extreme weather conditions, addressing the problem of large deviations in predictions in extreme weather conditions in the prior art.

This invention discloses a two-stage power characteristic prediction method for wind farms and wind turbines under cold wave meteorological conditions. The method includes: establishing a cold wave meteorological identification model to achieve accurate identification of cold wave weather; constructing wind speed-power characteristic curves with environmental parameter corrections and establishing a CFD simulation model for each wind turbine; then, establishing a turbine power prediction model by combining flow field characteristics and meteorological data, and using optimization algorithms to improve the model's accuracy and robustness; and constructing an overall wind farm power prediction model using the LightGBM machine learning algorithm to obtain the predicted power of the entire wind farm and individual turbines. This invention can significantly improve the accuracy and reliability of wind power prediction, reducing the operational risks of wind farms and the instability factors of the power system caused by prediction errors. Its functions include accurately predicting and evaluating the performance of wind turbines under different cold meteorological conditions, providing strong support for the optimized operation of wind farms and the stable operation of the power system.

The invention relates to an air temperature and cold wave index evaluation method for the ice condition of a water conveyance canal in a needle cold region. The method comprises the following steps: collecting and collecting air temperature data along a canal; calculating the probability distribution of the daily average minimum temperature change; calculating a temperature cold wave index TCI; defining cold wave classification grade indexes and threshold values; and TCI analysis. According to the method, a three-parameter Log-logistic distribution function is adopted to describe the change of the air temperature, then normalization processing is carried out, and finally the cumulative frequency of the air temperature is introduced into river and canal line cold wave classification. On the basis of researching climate and air temperature characteristics and distribution rules along the canal, an air temperature and cold wave index method and a cold wave grade classification standard are established, and corresponding relations among different grades of cold waves, negative accumulated temperature thresholds and ice condition occurrence and development conditions are analyzed. The accumulated negative accumulated temperature threshold value and the cold wave grade classification method proposed by the air temperature and cold wave index method are more suitable for judgment and prediction of the ice condition development process, and a powerful scientific basis is provided for prediction of disaster prevention and reduction of river and canal ice conditions.

The invention relates to a winter cold-tide weather typing method based on the occurrence probability of an icing event of a power transmission line. The method comprises the following steps: acquiring initial power transmission line icing data and potential height data of a target area; clustering processing is carried out on the potential height data, and weather conditions of the target area are divided into a plurality of weather types; performing spatial interpolation on the initial power transmission line icing data to obtain target power transmission line icing data; according to the target power transmission line icing data and the various weather types, the occurrence probability of the power transmission line icing event under each weather type and the occurrence frequency of the power transmission line icing event under each weather type are obtained; and determining a target weather type corresponding to the power transmission line icing event in the target area according to the occurrence probability and the occurrence frequency of the power transmission line icing event in each weather type. The method can effectively improve the recognition precision of the weather type related to the icing event of the power transmission line.

The present invention discloses an ultra-short-term wind power segmented prediction method and system taking into account extreme cold wave weather samples, which relates to the technical field of wind power prediction, including using a sequence variational autoencoder (SeqVAE) algorithm to generate numerical weather forecast data and corresponding power samples. Then, a method based on a graph convolution (GCN) network and a bidirectional gated recurrent unit (BiGRU) is used to extract the cold wave power loss period in the entire period, and on this basis, a LightGBM prediction method is used for the normal weather period; a LightGBM‑Transformer prediction method is proposed for the power loss period to quantify the power loss generated. The present invention improves the accuracy of wind power prediction by performing combined power prediction separately for normal weather and cold wave weather.

The application discloses a kind of wax pool foundation-based building temperature adjustment, emergency heating and subsidence monitoring method, belong to building safety environmental protection energy saving field.The method is driven by roof solar photovoltaic panel in the ground wax heat storage pool, while realizing the comprehensive function of building day and night temperature regulation, cold wave emergency heating and ground subsidence monitoring.During the day, solar energy is used to heat the ground wax, which changes its phase and stores heat, and releases heat at night to ensure stable room temperature and reduce dependence on battery energy storage.In cold weather, the wax can be used as fuel for emergency heating to ensure that the indoor temperature remains at a livable temperature in extreme weather.In addition, the system monitors the wax level changes to dynamically monitor and warn the ground subsidence, ensuring the safety of the building.The application has the advantages of low cost, environmental protection, economy and strong adaptability, and is suitable for widespread use in rural single-story buildings.

This invention discloses a method for short-term wind power prediction under low-temperature and cold wave weather. In the feature extraction and selection stage, meteorological data around the wind farm are collected, feature correlation values ​​are calculated, and key features are screened. Subsequently, a meteorological-power mapping model is established using a Support Vector Machine (SVM) model and a Radial Basis Function (RBF) kernel. The model is then optimized through training, validation, and evaluation index analysis. For low-temperature and cold wave weather, comprehensive judgment conditions are set, and model parameters are adaptively adjusted based on prediction errors to improve model adaptability. Simultaneously, a real-time data-driven update mechanism is established, collecting data in real time and incrementally training the model. Performance is evaluated and feedback adjustments are made, forming a closed-loop optimization system. This method effectively improves the accuracy of wind power prediction under low-temperature and cold wave weather, enhances model adaptability and stability, provides strong support for wind power grid connection and grid dispatch, helps improve wind power operating efficiency, and promotes the development of the new energy power generation field.

The present invention discloses a method for predicting photovoltaic power generation under cold wave weather with semi-supervised learning, which comprises the following steps: (1) obtaining the historical power generation data and the synchronous meteorological data of a photovoltaic power station, and defining the cold wave discrimination criteria based on the characteristics of sudden temperature drop; (2) detecting and removing outliers by using the Grubbs test, complementing the missing data through the piecewise cubic Hermite interpolation method, and constructing a cold wave small sample data set; (3) using the SMOTE algorithm to expand the data of the cold wave small samples, screening high-confidence samples by combining K-medoids clustering, and training and initializing the CNN-LSTM model with the labeled data; (4) optimizing the parameters of the CNN-LSTM model by using the whale optimization algorithm WOA, finally outputting the photovoltaic power prediction result and conducting error verification; the present invention has important significance for the technical research in the fields of new energy power prediction, power grid dispatching, etc.

The present disclosure provides an active anti-freezing concrete panel for a rockfill dam and a method for casting the concrete panel for a rockfill dam. The concrete panel for the rockfill dam includes a panel body made of conductive concrete. Two electrodes electrically connected to an external circuit are provided inside the panel body. A shielding area is provided on the panel body between the two electrodes. The panel body is constructed to form a current in an area between the two electrodes and outside the shielding area, so that the panel body is conductive with the external circuit, thereby ensuring the mechanical properties of the panel body while reducing the heat loss of the heating layer, thereby reducing the operating cost of the panel body, achieving early preheating before the arrival of a cold wave, and adopting flexible power supply according to the intensity of the cold wave, thereby minimizing freezing damage and ensuring the safe and normal operation of the dam.

The present application belongs to the technical field of poultry house temperature prediction, and specifically discloses a poultry house temperature prediction method and system. The present application first constructs a multi-factor coupled high-density feature system, breaking through the limitation of traditional single feature input. Secondly, a time series prediction model integrating attention mechanism and environmental self-adaptive mechanism is designed. Through the feature attention layer, the key factors are dynamically weighted, solving the problem of the contribution degree of multiple source factors changing over time. By using TCN inflation convolution to efficiently extract local fluctuations, and by using the LSTM gating mechanism to capture long-time dependence, the deep mining of the evolution law of complex thermal environment is realized. In addition, the present application proposes an outdoor temperature driven environmental self-adaptive mechanism, taking the outdoor temperature change rate as the trigger signal of environmental sudden change, and dynamically fusing multi-scale receptive fields through the gating network, solving the prediction lag problem of traditional fixed window model at key moments such as sunset and cold wave.

The application discloses a kind of short-term wind power prediction methods under considering cold wave meteorological conditions, belong to short-term wind power prediction technical field, the specific steps of the prediction method are as follows: I, select wind farm power station historical wind power data and meteorological data time sequence, and pre-process input wind farm meteorological data and power data;II, temperature change trend is predicted using Prophet algorithm, combined with cold wave early warning, extract cold weather period of time;III, through DoppelGANger network, cold weather period sample data is expanded, and network parameter is set;The application can improve the fidelity of the generated model, improve the accuracy of multivariate time series prediction, improve the adaptability of the model in complex meteorological environment, improve the adaptability of the model in complex meteorological environment.

The invention belongs to the technical field of afforestation, particularly relates to an afforestation method for improving the defense capability of betula alnoides to extreme low-temperature weather, and provides a scheme aiming at the phenomenon that the defense capability of typical precious tree species in south China to extreme low-temperature weather cannot be improved in the prior art, and the method comprises the following steps: S1, constructing a uniform forest stand environment; constructing a locally uniform forest stand environment through uniform intermediate cutting with different intensities; and S2, constructing a heterogeneous forest stand environment. According to the method, the defense capacity of the typical precious tree species betula alnoides in the south China to extreme low-temperature weather is improved, meteorological characteristics in the extreme cold wave occurrence period and the cold resistance difference between the tree species in different afforestation modes are found out, the appropriate afforestation mode is selected, growth health and cold resistance are synchronously improved, and the method is suitable for large-scale popularization and application. The method promotes healthy and sustainable development of man-made forests of main precious tree species in South China, and is an important technology for actively coping with the influence of extreme low-temperature weather in the field of precious tree species cultivation.

The invention provides a unit heat supply steam extraction intelligent analysis system which comprises a multi-mode sensing network, a virtual energy storage unit set, a dynamic energy storage evaluation engine, a multi-target optimization controller and a fault diagnosis and redundancy control module. The fluctuation range of the return water temperature is compressed to 0.7 time of a design value under the load sudden change working condition in the cold-wave period, and meanwhile, the fault response time of the system is shortened by 80% through redundancy control achieved through space-time correlation analysis. Therefore, the energy utilization rate of the heat supply system under the extreme working condition is greatly improved, and the operation and maintenance cost is greatly reduced.

The invention belongs to the technical field of electric power weather forecast, and discloses a power grid facility-oriented cold wave disaster process risk assessment method and system, and the method comprises the steps: collecting field data outputted by a numerical weather forecast model, and recognizing and analyzing the key weather system and evolution characteristics of cold waves; the method comprises the following steps: constructing a GIS spatial analysis framework comprising a meteorological element graph layer and a power grid facility graph layer on the basis of a key weather system and evolution characteristics of cold waves, and performing overlay analysis on the GIS spatial analysis framework to obtain meteorological element distribution of an area where power grid facilities are located; and based on the meteorological element distribution of the area where the power grid facility is located, combining preset vulnerability parameters of the power grid facility and disaster characteristics of meteorological elements, constructing a risk assessment model, and outputting a risk assessment result. Accurate analysis, evaluation and early warning of the influence of the cold wave disaster on the power grid facilities are realized.